Abstract
Decision makers involved in list selection and purchase for direct marketing campaigns typically use sample response rates as a barometer of future success for a rollout effort. In some instances, decision makers may be convinced that sample response rates are not indicative of the true response rates for a given mailing list. Bayesian statistics present a methodology for revising test sample results using prior assessments and opinions on the likelihood of a particular response rate for given lists. Bayes estimation for updating response probabilities and an illustration of Bayes’ technique in direct marketing are presented.
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